Role of MicroRNAs in Anxiety and Anxiety-Related Disorders

MicroRNAs as critical regulators of gene expression important for functions including neuronal development, synapse formation, and synaptic plasticity have been linked with the regulation of neurobiological systems that underlie anxiety processing in the brain. In this chapter, we give an update on associative evidence linking regulation of microRNAs with anxiety- and trauma-related disorders. Moving beyond correlative research, functional studies have emerged recently that explore causal relationships between microRNA expression and anxiety-like behavior. It has been demonstrated that experimental up- or downregulation of the candidate microRNAs in important nodes of the anxiety neurocircuitry can indeed modulate anxiety-related behavior in animal models. Improved methodologies for assessing microRNA-mediated modulation have aided such functional studies, revealing a number of anxiety-regulating microRNAs including miR-15a, miR-17-92, miR-34, miR-101, miR-124, miR-135, and miR-155. Important functional target genes of these identified microRNAs are associated with specific neurotransmitter/neuromodulator signaling, neurotrophin (e.g., BDNF) expression and other aspects of synaptic plasticity, as well as with stress-regulatory/hypothalamic-pituitary-axis function. Furthermore, microRNAs have been revealed that are regulated in distinct brain regions following various anxiety-attenuating strategies. These include pharmacological treatments such as antidepressants and other drugs, as well as non-pharmacological interventions such as fear extinction/exposure therapy or positive stimuli such as exposure to environmental enrichment. These are first indications for a role for microRNAs in the mechanism of action of anxiolytic treatments. As research continues, there is much hope that a deeper understanding of the microRNA-mediated mechanisms underlying anxiety-related disorders could open up possibilities for future novel biomarker and treatment strategies.

Novel pharmacological targets in drug development for the treatment of anxiety and anxiety-related disorders

Current medication for anxiety disorders is suboptimal in terms of efficiency and tolerability, highlighting the need for improved drug treatments. In this review an overview of drugs being studied in different phases of clinical trials for their potential in the treatment of fear-, anxiety- and trauma-related disorders is presented. One strategy followed in drug development is refining and improving compounds interacting with existing anxiolytic drug targets, such as serotonergic and prototypical GABAergic benzodiazepines. A more innovative approach involves the search for compounds with novel mechanisms of anxiolytic action using the growing knowledge base concerning the relevant neurocircuitries and neurobiological mechanisms underlying pathological fear and anxiety. The target systems evaluated in clinical trials include glutamate, endocannabinoid and neuropeptide systems, as well as ion channels and targets derived from phytochemicals. Examples of promising novel candidates currently in clinical development for generalised anxiety disorder, social anxiety disorder, panic disorder, obsessive compulsive disorder or post-traumatic stress disorder include ketamine, riluzole, xenon with one common pharmacological action of modulation of glutamatergic neurotransmission, as well as the neurosteroid aloradine. Finally, compounds such as D-cycloserine, MDMA, L-DOPA and cannabinoids have shown efficacy in enhancing fear-extinction learning in humans. They are thus investigated in clinical trials as an augmentative strategy for speeding up and enhancing the long-term effectiveness of exposure-based psychotherapy, which could render chronic anxiolytic drug treatment dispensable for many patients. These efforts are indicative of a rekindled interest and renewed optimism in the anxiety drug discovery field, after decades of relative stagnation.

Initial validation of a transdiagnostic compulsivity questionnaire: the Cambridge-Chicago Compulsivity Trait Scale

OBJECTIVE

Compulsivity refers to a tendency toward repetitive habitual behaviors. Multiple disorders have compulsive symptoms at their core, including substance use disorders, gambling disorder, and obsessive-compulsive disorder. The aim of this study was to validate a scale for the objective, transdiagnostic measurement of compulsivity.

METHODS

The 15-item Cambridge-Chicago Compulsivity Trait Scale (CHI-T) was developed for the rapid but comprehensive measurement of compulsivity. Adults aged 18-29y were recruited using media advertisements, and completed the CHI-T in addition to demographic, clinical, and cognitive assessment. The validity and psychometric properties of the scale were quantified.

RESULTS

A total of 112 participants completed the study. The scale yielded a normal distribution with very few outliers. It had excellent psychometric properties, with high internal consistency (Cronbach's alpha=0.8), and excellent convergent validity against gold-standard assessments of compulsive symptoms (each p<0.001 for gambling disorder, obsessive-compulsive, and substance use disorder symptoms). Total scores on the scale correlated significantly with less risk-adjustment on the decision-making task (rigid response style), and divergent validity was confirmed against other cognitive domains (response inhibition and executive planning). The above significant findings withstood Bonferroni correction. Factor analysis suggested the existence of two latent factors: one related mainly to reward-seeking and the need for perfection, and the other relating to anxiolytic/soothing features of compulsivity.

CONCLUSION

The CHI-T, a scale designed to measure transdiagnostic compulsivity, appears to show excellent psychometric properties in a normative population and merits further investigation in the context of clinical patient populations, including in treatment trials.

Data Science in the Research Domain Criteria Era: Relevance of Machine Learning to the Study of Stress Pathology, Recovery, and Resilience

Diverse environmental and biological systems interact to influence individual differences in response to environmental stress. Understanding the nature of these complex relationships can enhance the development of methods to: (1) identify risk, (2) classify individuals as healthy or ill, (3) understand mechanisms of change, and (4) develop effective treatments. The Research Domain Criteria (RDoC) initiative provides a theoretical framework to understand health and illness as the product of multiple inter-related systems but does not provide a framework to characterize or statistically evaluate such complex relationships. Characterizing and statistically evaluating models that integrate multiple levels (e.g. synapses, genes, environmental factors) as they relate to outcomes that a free from prior diagnostic benchmarks represents a challenge requiring new computational tools that are capable to capture complex relationships and identify clinically relevant populations. In the current review, we will summarize machine learning methods that can achieve these goals.

A dimensional approach to modeling symptoms of neuropsychiatric disorders in the marmoset monkey

Some patients suffering from the same neuropsychiatric disorder may have no overlapping symptoms whilst others may share symptoms common to other distinct disorders. Therefore, the Research Domain Criteria initiative recognises the need for better characterisation of the individual symptoms on which to focus symptom-based treatment strategies. Many of the disorders involve dysfunction within the prefrontal cortex (PFC) and so the marmoset, due to their highly developed PFC and small size, is an ideal species for studying the neurobiological basis of the behavioural dimensions that underlie these symptoms.Here we focus on a battery of tests that address dysfunction spanning the cognitive (cognitive inflexibility and working memory), negative valence (fear generalisation and negative bias) and positive valence (anhedonia) systems pertinent for understanding disorders such as ADHD, Schizophrenia, Anxiety, Depression and OCD. Parsing the separable prefrontal and striatal circuits and identifying the selective neurochemical modulation (serotonin vs dopamine) that underlie cognitive dysfunction have revealed counterparts in the clinical domain. Aspects of the negative valence system have been explored both at individual- (trait anxiety and genetic variation in serotonin transporter) and circuit-based levels enabling the understanding of generalisation processes, negative biases and differential responsiveness to SSRIs. Within the positive valence system, the combination of cardiovascular and behavioural measures provides a framework for understanding motivational, anticipatory and consummatory aspects of anhedonia and their neurobiological mechanisms. Together, the direct comparison of experimental findings in marmosets with clinical studies is proving an excellent translational model to address the behavioural dimensions and neurobiology of neuropsychiatric symptoms. © 2016 Wiley Periodicals, Inc. Develop Neurobiol 77: 328-353, 2017.